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3 Getting Started with R for Data Analysis

The book uses R for statistical analyses (http://www.r-project.org). R is a free software environment; you can download it at no charge here: https://cran.r-project.org.

3.1 Initial Setup

To get started, follow the following steps:

Install R: https://cran.r-project.org
Install RStudio Desktop: https://posit.co/download/rstudio-desktop
After installing RStudio, open RStudio and run the following code in the console to install several key R packages:

Code

install.packages(c("petersenlab","tidyverse","psych"))

Note 3.1: If you are in Dr. Petersen’s class

If you are in Dr. Petersen’s class, also perform the following steps:

Set up a free account on GitHub.com.
Download GitHub Desktop: https://desktop.github.com

3.2 Installing Packages

You can install R packages using the following syntax:

Code

install.packages("INSERT_PACKAGE_NAME_HERE")

For instance, you can use the following code to install the nflreadr package:

Code

install.packages("nflreadr")

3.3 Load Packages

Code

library("nflreadr")
library("nflfastR")
library("nflplotR")
library("progressr")
library("lubridate")
library("tidyverse")

3.4 Download Football Data

3.4.1 Players

Code

nfl_players <- progressr::with_progress(
  nflreadr::load_players())

3.4.2 Teams

Code

nfl_teams <- progressr::with_progress(
  nflreadr::load_teams(current = TRUE))

3.4.3 Player Info

3.4.4 Rosters

A Data Dictionary for rosters is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_rosters.html

Code

nfl_rosters <- progressr::with_progress(
  nflreadr::load_rosters(seasons = TRUE))

nfl_rosters_weekly <- progressr::with_progress(
  nflreadr::load_rosters_weekly(seasons = TRUE))

3.4.5 Game Schedules

A Data Dictionary for game schedules data is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_schedules.html

Code

nfl_schedules <- progressr::with_progress(
  nflreadr::load_schedules(seasons = TRUE))

3.4.6 The Combine

A Data Dictionary for data from the combine is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_combine.html

Code

nfl_combine <- progressr::with_progress(
  nflreadr::load_combine(seasons = TRUE))

3.4.7 Draft Picks

A Data Dictionary for draft picks data is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_draft_picks.html

Code

nfl_draftPicks <- progressr::with_progress(
  nflreadr::load_draft_picks(seasons = TRUE))

3.4.8 Depth Charts

A Data Dictionary for data from weekly depth charts is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_depth_charts.html

Code

nfl_depthCharts <- progressr::with_progress(
  nflreadr::load_depth_charts(seasons = TRUE))

3.4.9 Play-By-Play Data

To download play-by-play data from prior weeks and seasons, we can use the load_pbp() function of the nflreadr package. We add a progress bar using the with_progress() function from the progressr package because it takes a while to run. A Data Dictionary for the play-by-play data is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_pbp.html

Note 3.2: Downloading play-by-play data

Note: the following code takes a while to run.

Code

nfl_pbp <- progressr::with_progress(
  nflreadr::load_pbp(seasons = TRUE))

3.4.10 Participation

A Data Dictionary for the participation data is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_participation.html

Code

nfl_participation <- progressr::with_progress(
  nflreadr::load_participation(
    seasons = TRUE,
    include_pbp = TRUE))

3.4.11 Historical Weekly Actual Player Statistics

We can download historical week-by-week actual player statistics using the load_player_stats() function from the nflreadr package. A Data Dictionary for statistics for offensive players is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_player_stats.html. A Data Dictionary for statistics for defensive players is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_player_stats_def.html.

Code

nfl_actualStats_offense_weekly <- progressr::with_progress(
  nflreadr::load_player_stats(
    seasons = TRUE,
    stat_type = "offense"))

nfl_actualStats_defense_weekly <- progressr::with_progress(
  nflreadr::load_player_stats(
    seasons = TRUE,
    stat_type = "defense"))

nfl_actualStats_kicking_weekly <- progressr::with_progress(
  nflreadr::load_player_stats(
    seasons = TRUE,
    stat_type = "kicking"))

3.4.12 Injuries

A Data Dictionary for injury data is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_injuries.html

Code

nfl_injuries <- progressr::with_progress(
  nflreadr::load_injuries(seasons = TRUE))

3.4.13 Snap Counts

A Data Dictionary for snap counts data is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_snap_counts.html

Code

nfl_snapCounts <- progressr::with_progress(
  nflreadr::load_snap_counts(seasons = TRUE))

3.4.14 ESPN QBR

A Data Dictionary for ESPN QBR data is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_espn_qbr.html

Code

nfl_espnQBR_seasonal <- progressr::with_progress(
  nflreadr::load_espn_qbr(
    seasons = TRUE,
    summary_type = c("season")))

nfl_espnQBR_weekly <- progressr::with_progress(
  nflreadr::load_espn_qbr(
    seasons = TRUE,
    summary_type = c("weekly")))

nfl_espnQBR_weekly$game_week <- as.character(nfl_espnQBR_weekly$game_week)

nfl_espnQBR <- bind_rows(
  nfl_espnQBR_seasonal,
  nfl_espnQBR_weekly
)

3.4.15 NFL Next Gen Stats

A Data Dictionary for NFL Next Gen Stats data is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_nextgen_stats.html

Code

nfl_nextGenStats_pass_weekly <- progressr::with_progress(
  nflreadr::load_nextgen_stats(
    seasons = TRUE,
    stat_type = c("passing")))

nfl_nextGenStats_rush_weekly <- progressr::with_progress(
  nflreadr::load_nextgen_stats(
    seasons = TRUE,
    stat_type = c("rushing")))

nfl_nextGenStats_rec_weekly <- progressr::with_progress(
  nflreadr::load_nextgen_stats(
    seasons = TRUE,
    stat_type = c("receiving")))

nfl_nextGenStats_weekly <- bind_rows(
  nfl_nextGenStats_pass_weekly,
  nfl_nextGenStats_rush_weekly,
  nfl_nextGenStats_rec_weekly
)

3.4.16 Advanced Stats from PFR

A Data Dictionary for PFR passing data is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_pfr_passing.html

Code

nfl_advancedStatsPFR_pass_seasonal <- progressr::with_progress(
  nflreadr::load_pfr_advstats(
    seasons = TRUE,
    stat_type = c("pass"),
    summary_level = c("season")))

nfl_advancedStatsPFR_pass_weekly <- progressr::with_progress(
  nflreadr::load_pfr_advstats(
    seasons = TRUE,
    stat_type = c("pass"),
    summary_level = c("week")))

nfl_advancedStatsPFR_rush_seasonal <- progressr::with_progress(
  nflreadr::load_pfr_advstats(
    seasons = TRUE,
    stat_type = c("rush"),
    summary_level = c("season")))

nfl_advancedStatsPFR_rush_weekly <- progressr::with_progress(
  nflreadr::load_pfr_advstats(
    seasons = TRUE,
    stat_type = c("rush"),
    summary_level = c("week")))

nfl_advancedStatsPFR_rec_seasonal <- progressr::with_progress(
  nflreadr::load_pfr_advstats(
    seasons = TRUE,
    stat_type = c("rec"),
    summary_level = c("season")))

nfl_advancedStatsPFR_rec_weekly <- progressr::with_progress(
  nflreadr::load_pfr_advstats(
    seasons = TRUE,
    stat_type = c("rec"),
    summary_level = c("week")))

nfl_advancedStatsPFR_def_seasonal <- progressr::with_progress(
  nflreadr::load_pfr_advstats(
    seasons = TRUE,
    stat_type = c("def"),
    summary_level = c("season")))

nfl_advancedStatsPFR_def_weekly <- progressr::with_progress(
  nflreadr::load_pfr_advstats(
    seasons = TRUE,
    stat_type = c("def"),
    summary_level = c("week")))

nfl_advancedStatsPFR <- bind_rows(
  nfl_advancedStatsPFR_pass_seasonal,
  nfl_advancedStatsPFR_pass_weekly,
  nfl_advancedStatsPFR_rush_seasonal,
  nfl_advancedStatsPFR_rush_weekly,
  nfl_advancedStatsPFR_rec_seasonal,
  nfl_advancedStatsPFR_rec_weekly,
  nfl_advancedStatsPFR_def_seasonal,
  nfl_advancedStatsPFR_def_weekly,
)

3.4.17 Player Contracts

A Data Dictionary for player contracts data is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_contracts.html

Code

nfl_playerContracts <- progressr::with_progress(
  nflreadr::load_contracts())

3.4.18 FTN Charting Data

A Data Dictionary for FTN Charting data is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_ftn_charting.html

Code

nfl_ftnCharting <- progressr::with_progress(
  nflreadr::load_ftn_charting(seasons = TRUE))

3.4.19 Fantasy Player IDs

A Data Dictionary for fantasy player ID data is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_ff_playerids.html

Code

nfl_playerIDs <- progressr::with_progress(
  nflreadr::load_ff_playerids())

3.4.20 FantasyPros Rankings

A Data Dictionary for FantasyPros ranking data is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_ff_rankings.html

Code

#nfl_rankings <- progressr::with_progress( # currently throws error
#  nflreadr::load_ff_rankings(type = "all"))

nfl_rankings_draft <- progressr::with_progress(
  nflreadr::load_ff_rankings(type = "draft"))

nfl_rankings_weekly <- progressr::with_progress(
  nflreadr::load_ff_rankings(type = "week"))

nfl_rankings <- bind_rows(
  nfl_rankings_draft,
  nfl_rankings_weekly
)

3.4.21 Expected Fantasy Points

A Data Dictionary for expected fantasy points data is located at the following link: https://nflreadr.nflverse.com/articles/dictionary_ff_opportunity.html

Code

nfl_expectedFantasyPoints_weekly <- progressr::with_progress(
  nflreadr::load_ff_opportunity(
    seasons = TRUE,
    stat_type = "weekly",
    model_version = "latest"
  ))

nfl_expectedFantasyPoints_pass <- progressr::with_progress(
  nflreadr::load_ff_opportunity(
    seasons = TRUE,
    stat_type = "pbp_pass",
    model_version = "latest"
  ))

nfl_expectedFantasyPoints_rush <- progressr::with_progress(
  nflreadr::load_ff_opportunity(
    seasons = TRUE,
    stat_type = "pbp_rush",
    model_version = "latest"
  ))

nfl_expectedFantasyPoints_weekly$season <- as.integer(nfl_expectedFantasyPoints_weekly$season)

nfl_expectedFantasyPoints_offense <- bind_rows(
  nfl_expectedFantasyPoints_pass,
  nfl_expectedFantasyPoints_rush
)

3.5 Data Dictionary

Data Dictionaries are metadata that describe the meaning of the variables in a datset. You can find Data Dictionaries for the various NFL datasets at the following link: https://nflreadr.nflverse.com/articles/index.html.

3.6 Variable Names

To see the names of variables in a data frame, use the following syntax:

Code

names(nfl_players)

 [1] "status"                   "display_name"            
 [3] "first_name"               "last_name"               
 [5] "esb_id"                   "gsis_id"                 
 [7] "suffix"                   "birth_date"              
 [9] "college_name"             "position_group"          
[11] "position"                 "jersey_number"           
[13] "height"                   "weight"                  
[15] "years_of_experience"      "team_abbr"               
[17] "team_seq"                 "current_team_id"         
[19] "football_name"            "entry_year"              
[21] "rookie_year"              "draft_club"              
[23] "college_conference"       "status_description_abbr" 
[25] "status_short_description" "gsis_it_id"              
[27] "short_name"               "smart_id"                
[29] "headshot"                 "draft_number"            
[31] "uniform_number"           "draft_round"             
[33] "season"

3.7 Logical Operators

3.7.1 Is Equal To: `==`

Code

nfl_players$position_group == "RB"

3.7.2 Is Not Equal To: `!=`

Code

nfl_players$position_group != "RB"

3.7.3 Is Greater Than: `>`

Code

nfl_players$weight > 300

3.7.4 Is Less Than: `<`

Code

nfl_players$weight < 300

3.7.5 Is Greater Than or Equal To: `>=`

Code

nfl_players$weight >= 300

3.7.6 Is Less Than or Equal To: `<=`

Code

nfl_players$weight <= 300

3.7.7 Is In a Value of Another Vector: `%in%`

Code

nfl_players$position_group %in% c("QB","RB","WR")

3.7.8 Is Not In a Value of Another Vector: `!(%in%)`

Code

!(nfl_players$position_group %in% c("QB","RB","WR"))

3.7.9 Is Missing: `is.na()`

Code

is.na(nfl_players$college_name)

3.7.10 Is Not Missing: `!is.na()`

Code

!is.na(nfl_players$college_name)

3.7.11 And: `&`

Code

nfl_players$position_group == "RB" & nfl_players$weight > 230

3.7.12 Or: `|`

Code

nfl_players$position_group == "RB" | nfl_players$weight > 230

3.8 Subset

To subset a data frame, use brackets to specify the subset of rows and columns to keep, where the value/vector before the comma specifies the rows to keep, and the value/vector after the comma specifies the columns to keep:

Code

dataframe[rowsToKeep, columnsToKeep]

You can subset by using any of the following:

numeric indices of the rows/columns to keep (or drop)
names of the rows/columns to keep (or drop)
values of TRUE and FALSE corresponding to which rows/columns to keep

3.8.1 One Variable

To subset one variable, use the following syntax:

Code

nfl_players$display_name

or:

Code

nfl_players[,"display_name"]

3.8.2 Particular Rows of One Variable

To subset one variable, use the following syntax:

Code

nfl_players$display_name[which(nfl_players$position_group == "RB")]

or:

Code

nfl_players[which(nfl_players$position_group == "RB"), "display_name"]

3.8.3 Particular Columns (Variables)

To subset particular columns/variables, use the following syntax:

3.8.3.1 Base `R`

Code

subsetVars <- c("status","display_name","college_name")

nfl_players[,c(1,2,9)]

Code

nfl_players[,c("status","display_name","college_name")]

Code

nfl_players[,subsetVars]

Or, to drop columns:

Code

dropVars <- c("status","college_name")

nfl_players[,-c(1,9)]

Code

nfl_players[,!(names(nfl_players) %in% c("status","college_name"))]

Code

nfl_players[,!(names(nfl_players) %in% dropVars)]

3.8.3.2 Tidyverse

Code

nfl_players %>%
  select(status, display_name, college_name)

Code

nfl_players %>%
  select(status:college_name)

Code

nfl_players %>%
  select(all_of(subsetVars))

Or, to drop columns:

Code

nfl_players %>%
  select(-status, -college_name)

Code

nfl_players %>%
  select(-c(status:college_name))

Code

nfl_players %>%
  select(-all_of(dropVars))

3.8.4 Particular Rows

To subset particular rows, use the following syntax:

3.8.4.1 Base `R`

Code

subsetRows <- c(1,3,5)

nfl_players[c(1,3,5),]

Code

nfl_players[subsetRows,]

Code

nfl_players[which(nfl_players$position_group == "RB"),]

3.8.4.2 Tidyverse

Code

nfl_players %>%
  filter(position_group == "RB")

Code

nfl_players %>%
  filter(position_group == "RB", weight <= 250)

Code

nfl_players %>%
  filter(position_group == "RB" | weight >= 250)

3.8.5 Particular Rows and Columns

To subset particular rows and columns, use the following syntax:

3.8.5.1 Base `R`

Code

nfl_players[c(1,3,5), c(1,2,3)]

Code

nfl_players[subsetRows, subsetVars]

Code

nfl_players[which(nfl_players$position_group == "RB"), subsetVars]

3.8.5.2 Tidyverse

Code

nfl_players %>%
  filter(position_group == "RB") %>%
  select(all_of(subsetVars))

3.9 View Data

3.9.1 All Data

To view data, use the following syntax:

Code

View(nfl_players)

3.9.2 First 6 Rows/Elements

To view only the first six rows (if a data frame) or elements (if a vector), use the following syntax:

Code

head(nfl_players)

Code

head(nfl_players$display_name)

[1] "'Omar Ellison"    "A'Shawn Robinson" "A.J. Arcuri"      "A.J. Bouye"      
[5] "A.J. Brown"       "A.J. Cann"

3.10 Data Characteristics

3.10.1 Data Structure

Code

str(nfl_players)

nflvrs_d [20,039 × 33] (S3: nflverse_data/tbl_df/tbl/data.table/data.frame)
 $ status                  : chr [1:20039] "RET" "ACT" "ACT" "RES" ...
 $ display_name            : chr [1:20039] "'Omar Ellison" "A'Shawn Robinson" "A.J. Arcuri" "A.J. Bouye" ...
 $ first_name              : chr [1:20039] "'Omar" "A'Shawn" "A.J." "Arlandus" ...
 $ last_name               : chr [1:20039] "Ellison" "Robinson" "Arcuri" "Bouye" ...
 $ esb_id                  : chr [1:20039] "ELL711319" "ROB367960" "ARC716900" "BOU651714" ...
 $ gsis_id                 : chr [1:20039] "00-0004866" "00-0032889" "00-0037845" "00-0030228" ...
 $ suffix                  : chr [1:20039] NA NA NA NA ...
 $ birth_date              : chr [1:20039] NA "1995-03-21" NA "1991-08-16" ...
 $ college_name            : chr [1:20039] NA "Alabama" "Michigan State" "Central Florida" ...
 $ position_group          : chr [1:20039] "WR" "DL" "OL" "DB" ...
 $ position                : chr [1:20039] "WR" "DT" "T" "CB" ...
 $ jersey_number           : int [1:20039] 84 91 61 24 11 60 6 81 63 20 ...
 $ height                  : num [1:20039] 73 76 79 72 72 75 76 69 76 72 ...
 $ weight                  : int [1:20039] 200 330 320 191 226 325 220 190 280 183 ...
 $ years_of_experience     : chr [1:20039] "2" "8" "2" "8" ...
 $ team_abbr               : chr [1:20039] "LAC" "NYG" "LA" "CAR" ...
 $ team_seq                : int [1:20039] NA 1 NA 1 1 1 1 NA NA NA ...
 $ current_team_id         : chr [1:20039] "4400" "3410" "2510" "0750" ...
 $ football_name           : chr [1:20039] NA "A'Shawn" "A.J." "A.J." ...
 $ entry_year              : int [1:20039] NA 2016 2022 2013 2019 2015 2019 NA NA NA ...
 $ rookie_year             : int [1:20039] NA 2016 2022 2013 2019 2015 2019 NA NA NA ...
 $ draft_club              : chr [1:20039] NA "DET" "LA" NA ...
 $ college_conference      : chr [1:20039] NA "Southeastern Conference" "Big Ten Conference" "American Athletic Conference" ...
 $ status_description_abbr : chr [1:20039] NA "A01" "A01" "R01" ...
 $ status_short_description: chr [1:20039] NA "Active" "Active" "R/Injured" ...
 $ gsis_it_id              : int [1:20039] NA 43335 54726 40688 47834 42410 48335 NA NA NA ...
 $ short_name              : chr [1:20039] NA "A.Robinson" "A.Arcuri" "A.Bouye" ...
 $ smart_id                : chr [1:20039] "3200454c-4c71-1319-728e-d49d3d236f8f" "3200524f-4236-7960-bf20-bc060ac0f49c" "32004152-4371-6900-5185-8cdd66b2ad11" "3200424f-5565-1714-cb38-07c822111a12" ...
 $ headshot                : chr [1:20039] NA "https://static.www.nfl.com/image/private/f_auto,q_auto/league/qgiwxchd1lmgszfunys8" NA "https://static.www.nfl.com/image/private/f_auto,q_auto/league/cpgi2hbhnmvs1oczkzas" ...
 $ draft_number            : int [1:20039] NA 46 261 NA 51 67 NA NA NA NA ...
 $ uniform_number          : chr [1:20039] NA "91" "61" "24" ...
 $ draft_round             : chr [1:20039] NA NA NA NA ...
 $ season                  : int [1:20039] NA NA NA NA NA NA NA NA NA NA ...
 - attr(*, "nflverse_type")= chr "players"
 - attr(*, "nflverse_timestamp")= POSIXct[1:1], format: "2024-03-01 01:18:40"

3.10.2 Data Dimensions

Number of rows and columns:

Code

dim(nfl_players)

[1] 20039    33

3.10.3 Number of Elements

Code

length(nfl_players$display_name)

[1] 20039

3.10.4 Number of Missing Elements

Code

length(nfl_players$college_name[which(is.na(nfl_players$college_name))])

[1] 12127

3.10.5 Number of Non-Missing Elements

Code

length(nfl_players$college_name[which(!is.na(nfl_players$college_name))])

[1] 7912

Code

length(na.omit(nfl_players$college_name))

[1] 7912

3.11 Create New Variables

To create a new variable, use the following syntax:

Code

nfl_players$newVar <- NA

Here is an example of creating a new variable:

Code

nfl_players$newVar <- 1:nrow(nfl_players)

3.12 Create a Data Frame

Here is an example of creating a data frame:

Code

mydata <- data.frame(
  ID = c(1:5, 1047:1051),
  cat = sample(
    0:1,
    10,
    replace = TRUE)
)

mydata

3.13 Recode Variables

Here is an example of recoding a variable:

Code

mydata$oldVar1 <- NA
mydata$oldVar1[which(mydata$cat == 0)] <- "dog"
mydata$oldVar1[which(mydata$cat == 1)] <- "cat"

mydata$oldVar2 <- NA
mydata$oldVar2[which(mydata$cat == 0)] <- "no"
mydata$oldVar2[which(mydata$cat == 1)] <- "yes"

Recode multiple variables:

Code

mydata %>%
  mutate(across(c(
    oldVar1:oldVar2),
    ~ case_match(
      .,
      c("dog","no") ~ 0,
      c("cat","yes") ~ 1)))

3.14 Rename Variables

Code

mydata <- mydata %>% 
  rename(
    newVar1 = oldVar1,
    newVar2 = oldVar2)

Using a vector of variable names:

Code

varNamesFrom <- c("oldVar1","oldVar2")
varNamesTo <- c("newVar1","newVar2")

mydata <- mydata %>% 
  rename_with(~ varNamesTo, all_of(varNamesFrom))

3.15 Convert the Types of Variables

One variable:

Code

mydata$factorVar <- factor(mydata$ID)
mydata$numericVar <- as.numeric(mydata$cat)
mydata$integerVar <- as.integer(mydata$cat)
mydata$characterVar <- as.character(mydata$newVar1)

Multiple variables:

Code

mydata %>%
  mutate(across(c(
    ID,
    cat),
    as.numeric))

Code

mydata %>%
  mutate(across(
    ID:cat,
    as.numeric))

Code

mydata %>%
  mutate(across(where(is.factor), as.character))

3.16 Calculations

3.16.1 Historical Actual Player Statistics

In addition to week-by-week actual player statistics, we can also compute historical actual player statistics as a function of different timeframes, including season-by-season and career statistics.

3.16.1.1 Career Statistics

First, we can compute the players’ career statistics using the calculate_player_stats(), calculate_player_stats_def(), and calculate_player_stats_kicking() functions from the nflfastR package for offensive players, defensive players, and kickers, respectively.

Note 3.3: Calculating players’ career statistics

Note: the following code takes a while to run.

Code

nfl_actualStats_offense_career <- nflfastR::calculate_player_stats(
  nfl_pbp,
  weekly = FALSE)

nfl_actualStats_defense_career <- nflfastR::calculate_player_stats_def(
  nfl_pbp,
  weekly = FALSE)

nfl_actualStats_kicking_career <- nflfastR::calculate_player_stats_kicking(
  nfl_pbp,
  weekly = FALSE)

3.16.1.2 Season-by-Season Statistics

Second, we can compute the players’ season-by-season statistics.

Code

seasons <- unique(nfl_pbp$season)

nfl_pbp_seasonalList <- list()
nfl_actualStats_offense_seasonalList <- list()
nfl_actualStats_defense_seasonalList <- list()
nfl_actualStats_kicking_seasonalList <- list()

Note 3.4: Calculating players’ season-by-season statistics

Note: the following code takes a while to run.

Code

pb <- txtProgressBar(
  min = 0,
  max = length(seasons),
  style = 3)

for(i in 1:length(seasons)){
  # Subset play-by-play data by season
  nfl_pbp_seasonalList[[i]] <- nfl_pbp %>% 
    filter(season == seasons[i])
  
  # Compute actual statistics by season
  nfl_actualStats_offense_seasonalList[[i]] <- 
    nflfastR::calculate_player_stats(
      nfl_pbp_seasonalList[[i]],
      weekly = FALSE)

  nfl_actualStats_defense_seasonalList[[i]] <- 
    nflfastR::calculate_player_stats_def(
      nfl_pbp_seasonalList[[i]],
      weekly = FALSE)

  nfl_actualStats_kicking_seasonalList[[i]] <- 
    nflfastR::calculate_player_stats_kicking(
      nfl_pbp_seasonalList[[i]],
      weekly = FALSE)

  nfl_actualStats_offense_seasonalList[[i]]$season <- seasons[i]
  nfl_actualStats_defense_seasonalList[[i]]$season <- seasons[i]
  nfl_actualStats_kicking_seasonalList[[i]]$season <- seasons[i]
  
  print(
    paste("Completed computing projections for season: ", seasons[i], sep = ""))

  # Update the progress bar
  setTxtProgressBar(pb, i)
}

# Close the progress bar
close(pb)

nfl_actualStats_offense_seasonal <- nfl_actualStats_offense_seasonalList %>% 
  bind_rows()
nfl_actualStats_defense_seasonal <- nfl_actualStats_defense_seasonalList %>% 
  bind_rows()
nfl_actualStats_kicking_seasonal <- nfl_actualStats_kicking_seasonalList %>% 
  bind_rows()

3.16.1.3 Week-by-Week Statistics

We already load players’ week-by-week statistics above. Nevertheless, we could compute players’ weekly statistics from the play-by-play data using the following syntax:

Code

nfl_actualStats_offense_weekly <- nflfastR::calculate_player_stats(
  nfl_pbp,
  weekly = TRUE)

nfl_actualStats_defense_weekly <- nflfastR::calculate_player_stats_def(
  nfl_pbp,
  weekly = TRUE)

nfl_actualStats_kicking_weekly <- nflfastR::calculate_player_stats_kicking(
  nfl_pbp,
  weekly = TRUE)

3.16.2 Historical Actual Fantasy Points

3.16.3 Player Age

Code

# Reshape from wide to long format
nfl_actualStats_offense_weekly_long <- nfl_actualStats_offense_weekly %>% 
  pivot_longer(
    cols = c(recent_team, opponent_team),
    names_to = "role",
    values_to = "team")

# Perform separate inner join operations for the home_team and away_team
nfl_actualStats_offense_weekly_home <- inner_join(
  nfl_actualStats_offense_weekly_long,
  nfl_schedules,
  by = c("season","week","team" = "home_team")) %>% 
  mutate(home_away = "home_team")

nfl_actualStats_offense_weekly_away <- inner_join(
  nfl_actualStats_offense_weekly_long,
  nfl_schedules,
  by = c("season","week","team" = "away_team")) %>% 
  mutate(home_away = "away_team")

nfl_actualStats_defense_weekly_home <- inner_join(
  nfl_actualStats_defense_weekly,
  nfl_schedules,
  by = c("season","week","team" = "home_team")) %>% 
  mutate(home_away = "home_team")

nfl_actualStats_defense_weekly_away <- inner_join(
  nfl_actualStats_defense_weekly,
  nfl_schedules,
  by = c("season","week","team" = "away_team")) %>% 
  mutate(home_away = "away_team")

nfl_actualStats_kicking_weekly_home <- inner_join(
  nfl_actualStats_kicking_weekly,
  nfl_schedules,
  by = c("season","week","team" = "home_team")) %>% 
  mutate(home_away = "home_team")

nfl_actualStats_kicking_weekly_away <- inner_join(
  nfl_actualStats_kicking_weekly,
  nfl_schedules,
  by = c("season","week","team" = "away_team")) %>% 
  mutate(home_away = "away_team")

# Combine the results of the join operations
nfl_actualStats_offense_weekly_schedules_long <- bind_rows(
  nfl_actualStats_offense_weekly_home,
  nfl_actualStats_offense_weekly_away)

nfl_actualStats_defense_weekly_schedules_long <- bind_rows(
  nfl_actualStats_defense_weekly_home,
  nfl_actualStats_defense_weekly_away)

nfl_actualStats_kicking_weekly_schedules_long <- bind_rows(
  nfl_actualStats_kicking_weekly_home,
  nfl_actualStats_kicking_weekly_away)

# Reshape from long to wide
player_game_gameday_offense <- nfl_actualStats_offense_weekly_schedules_long %>%
  distinct(player_id, season, week, game_id, home_away, team, gameday) %>% #, .keep_all = TRUE
  pivot_wider(
    names_from = home_away,
    values_from = team)

player_game_gameday_defense <- nfl_actualStats_defense_weekly_schedules_long %>%
  distinct(player_id, season, week, game_id, home_away, team, gameday) %>% #, .keep_all = TRUE
  pivot_wider(
    names_from = home_away,
    values_from = team)

player_game_gameday_kicking <- nfl_actualStats_kicking_weekly_schedules_long %>%
  distinct(player_id, season, week, game_id, home_away, team, gameday) %>% #, .keep_all = TRUE
  pivot_wider(
    names_from = home_away,
    values_from = team)

# Merge player birthdate and the game date
player_game_birthdate_gameday_offense <- left_join(
  player_game_gameday_offense,
  unique(nfl_players[,c("gsis_id","birth_date")]),
  by = c("player_id" = "gsis_id")
)

player_game_birthdate_gameday_defense <- left_join(
  player_game_gameday_defense,
  unique(nfl_players[,c("gsis_id","birth_date")]),
  by = c("player_id" = "gsis_id")
)

player_game_birthdate_gameday_kicking <- left_join(
  player_game_gameday_kicking,
  unique(nfl_players[,c("gsis_id","birth_date")]),
  by = c("player_id" = "gsis_id")
)

player_game_birthdate_gameday_offense$birth_date <- ymd(player_game_birthdate_gameday_offense$birth_date)
player_game_birthdate_gameday_offense$gameday <- ymd(player_game_birthdate_gameday_offense$gameday)

player_game_birthdate_gameday_defense$birth_date <- ymd(player_game_birthdate_gameday_defense$birth_date)
player_game_birthdate_gameday_defense$gameday <- ymd(player_game_birthdate_gameday_defense$gameday)

player_game_birthdate_gameday_kicking$birth_date <- ymd(player_game_birthdate_gameday_kicking$birth_date)
player_game_birthdate_gameday_kicking$gameday <- ymd(player_game_birthdate_gameday_kicking$gameday)

# Calculate player's age for a given week as the difference between their birthdate and the game date
player_game_birthdate_gameday_offense$age <- interval(
  start = player_game_birthdate_gameday_offense$birth_date,
  end = player_game_birthdate_gameday_offense$gameday
) %>% 
  time_length(unit = "years")

player_game_birthdate_gameday_defense$age <- interval(
  start = player_game_birthdate_gameday_defense$birth_date,
  end = player_game_birthdate_gameday_defense$gameday
) %>% 
  time_length(unit = "years")

player_game_birthdate_gameday_kicking$age <- interval(
  start = player_game_birthdate_gameday_kicking$birth_date,
  end = player_game_birthdate_gameday_kicking$gameday
) %>% 
  time_length(unit = "years")

# Merge with player info
player_age_offense <-  left_join(
  player_game_birthdate_gameday_offense,
  nfl_players %>% select(-birth_date, -season),
  by = c("player_id" = "gsis_id"))

player_age_defense <-  left_join(
  player_game_birthdate_gameday_defense,
  nfl_players %>% select(-birth_date, -season),
  by = c("player_id" = "gsis_id"))

player_age_kicking <-  left_join(
  player_game_birthdate_gameday_kicking,
  nfl_players %>% select(-birth_date, -season),
  by = c("player_id" = "gsis_id"))

# Add game_id to weekly stats to facilitate merging
nfl_actualStats_game_offense_weekly <- nfl_actualStats_offense_weekly %>% 
  left_join(
    player_age_offense[,c("season","week","player_id","game_id")],
    by = c("season","week","player_id"))

nfl_actualStats_game_defense_weekly <- nfl_actualStats_defense_weekly %>% 
  left_join(
    player_age_offense[,c("season","week","player_id","game_id")],
    by = c("season","week","player_id"))

nfl_actualStats_game_kicking_weekly <- nfl_actualStats_kicking_weekly %>% 
  left_join(
    player_age_offense[,c("season","week","player_id","game_id")],
    by = c("season","week","player_id"))

# Merge with player weekly stats
player_age_stats_offense <- left_join(
  player_age_offense %>% select(-position, -position_group),
  nfl_actualStats_game_offense_weekly,
  by = c(c("season","week","player_id","game_id")))

player_age_stats_defense <- left_join(
  player_age_defense %>% select(-position, -position_group),
  nfl_actualStats_game_defense_weekly,
  by = c(c("season","week","player_id","game_id")))

player_age_stats_kicking <- left_join(
  player_age_kicking %>% select(-position, -position_group),
  nfl_actualStats_game_kicking_weekly,
  by = c(c("season","week","player_id","game_id")))

player_age_stats_offense$years_of_experience <- as.integer(player_age_stats_offense$years_of_experience)
player_age_stats_defense$years_of_experience <- as.integer(player_age_stats_defense$years_of_experience)
player_age_stats_kicking$years_of_experience <- as.integer(player_age_stats_kicking$years_of_experience)

# Merge player info with seasonal stats
player_seasonal_offense <- left_join(
  nfl_actualStats_offense_seasonal,
  nfl_players %>% select(-position, -position_group, -season),
  by = c("player_id" = "gsis_id")
)

player_seasonal_defense <- left_join(
  nfl_actualStats_defense_seasonal,
  nfl_players %>% select(-position, -position_group, -season),
  by = c("player_id" = "gsis_id")
)

player_seasonal_kicking <- left_join(
  nfl_actualStats_kicking_seasonal,
  nfl_players %>% select(-position, -position_group, -season),
  by = c("player_id" = "gsis_id")
)

# Calculate age
season_startdate <- nfl_schedules %>% 
  group_by(season) %>% 
  summarise(startdate = min(gameday, na.rm = TRUE))

player_seasonal_offense <- player_seasonal_offense %>% 
  left_join(
    season_startdate,
    by = "season"
  )

player_seasonal_defense <- player_seasonal_defense %>% 
  left_join(
    season_startdate,
    by = "season"
  )

player_seasonal_kicking <- player_seasonal_kicking %>% 
  left_join(
    season_startdate,
    by = "season"
  )

player_seasonal_offense$age <- interval(
  start = player_seasonal_offense$birth_date,
  end = player_seasonal_offense$startdate
) %>% 
  time_length(unit = "years")

player_seasonal_defense$age <- interval(
  start = player_seasonal_defense$birth_date,
  end = player_seasonal_defense$startdate
) %>% 
  time_length(unit = "years")

player_seasonal_kicking$age <- interval(
  start = player_seasonal_kicking$birth_date,
  end = player_seasonal_kicking$startdate
) %>% 
  time_length(unit = "years")

3.17 Plotting

3.17.1 Rushing Yards per Carry By Player Age

Code

# Prepare Data
rushing_attempts <- nfl_pbp %>% 
  dplyr::filter(
    season_type == "REG") %>% 
    filter(
      rush == 1,
      rush_attempt == 1,
      qb_scramble == 0,
      qb_dropback == 0,
      !is.na(rushing_yards))

rb_yardsPerCarry <- rushing_attempts %>% 
  group_by(rusher_id, season) %>% 
  summarise(
    ypc = mean(rushing_yards, na.rm = TRUE),
    rush_attempts = n(),
    .groups = "drop") %>% 
  ungroup() %>% 
  left_join(
    nfl_players %>% select(-season),
    by = c("rusher_id" = "gsis_id")
  ) %>% 
  filter(
    position_group == "RB",
    rush_attempts >= 50) %>% 
  left_join(
    season_startdate,
    by = "season"
  )

rb_yardsPerCarry$age <- interval(
  start = rb_yardsPerCarry$birth_date,
  end = rb_yardsPerCarry$startdate
) %>% 
  time_length(unit = "years")

# Create Plot
ggplot2::ggplot(
  data = rb_yardsPerCarry,
  ggplot2::aes(
    x = age,
    y = ypc)) +
  ggplot2::geom_point() +
  ggplot2::geom_smooth() +
  ggplot2::labs(
    x = "Rushing Back Age (years)",
    y = "Rushing Yards per Carry/season",
    title = "2023 NFL Rushing Yards Per Carry per Season by Player Age",
    subtitle = "(minimum 50 rushing attempts)"
  ) +
  ggplot2::theme_classic()

Code

# Subset Data
rb_seasonal <- player_seasonal_offense %>% 
  filter(position_group == "RB")

# Create Plot
ggplot2::ggplot(
  data = rb_seasonal,
  ggplot2::aes(
    x = age,
    y = rushing_epa)) +
  ggplot2::geom_point() +
  ggplot2::geom_smooth() +
  ggplot2::labs(
    x = "Rushing Back Age (years)",
    y = "Rushing EPA/season",
    title = "2023 NFL Rushing EPA per Season by Player Age"
  ) +
  ggplot2::theme_classic()

3.17.2 Defensive and Offensive EPA per Play

Expected points added (EPA) per play by the team with possession.

Code

pbp_regularSeason <- nfl_pbp %>% 
  dplyr::filter(
    season == 2023,
    season_type == "REG") %>%
  dplyr::filter(!is.na(posteam) & (rush == 1 | pass == 1))

epa_offense <- pbp_regularSeason %>%
  dplyr::group_by(team = posteam) %>%
  dplyr::summarise(off_epa = mean(epa, na.rm = TRUE))

epa_defense <- pbp_regularSeason %>%
  dplyr::group_by(team = defteam) %>%
  dplyr::summarise(def_epa = mean(epa, na.rm = TRUE))

epa_combined <- epa_offense %>%
  dplyr::inner_join(epa_defense, by = "team")

ggplot2::ggplot(
  data = epa_combined,
  ggplot2::aes(
    x = off_epa,
    y = def_epa)) +
  nflplotR::geom_mean_lines(
    ggplot2::aes(
      x0 = off_epa ,
      y0 = def_epa)) +
  nflplotR::geom_nfl_logos(
    ggplot2::aes(
      team_abbr = team),
      width = 0.065,
      alpha = 0.7) +
  ggplot2::labs(
    x = "Offense EPA/play",
    y = "Defense EPA/play",
    title = "2023 NFL Offensive and Defensive EPA per Play"
  ) +
  ggplot2::theme_classic() +
  ggplot2::scale_y_reverse()

Figure 3.1: 2023 NFL Offensive and Defensive EPA per Play

3.1 Initial Setup

3.2 Installing Packages

3.3 Load Packages

3.4 Download Football Data

3.4.1 Players

3.4.2 Teams

3.4.3 Player Info

3.4.4 Rosters

3.4.5 Game Schedules

3.4.6 The Combine

3.4.7 Draft Picks

3.4.8 Depth Charts

3.4.9 Play-By-Play Data

3.4.10 Participation

3.4.11 Historical Weekly Actual Player Statistics

3.4.12 Injuries

3.4.13 Snap Counts

3.4.14 ESPN QBR

3.4.15 NFL Next Gen Stats

3.4.16 Advanced Stats from PFR

3.4.17 Player Contracts

3.4.18 FTN Charting Data

3.4.19 Fantasy Player IDs

3.4.20 FantasyPros Rankings

3.4.21 Expected Fantasy Points

3.5 Data Dictionary

3.6 Variable Names

3.7 Logical Operators

3.7.1 Is Equal To: ==

3.7.2 Is Not Equal To: !=

3.7.3 Is Greater Than: >

3.7.4 Is Less Than: <

3.7.5 Is Greater Than or Equal To: >=

3.7.6 Is Less Than or Equal To: <=

3.7.7 Is In a Value of Another Vector: %in%

3.7.8 Is Not In a Value of Another Vector: !(%in%)

3.7.9 Is Missing: is.na()

3.7.10 Is Not Missing: !is.na()

3.7.11 And: &

3.7.12 Or: |

3.8 Subset

3.8.1 One Variable

3.8.2 Particular Rows of One Variable

3.8.3 Particular Columns (Variables)

3.8.3.1 Base R

3.8.3.2 Tidyverse

3.8.4 Particular Rows

3.8.4.1 Base R

3.8.4.2 Tidyverse

3.8.5 Particular Rows and Columns

3.8.5.1 Base R

3.8.5.2 Tidyverse

3.9 View Data

3.9.1 All Data

3.9.2 First 6 Rows/Elements

3.10 Data Characteristics

3.10.1 Data Structure

3.10.2 Data Dimensions

3.10.3 Number of Elements

3.10.4 Number of Missing Elements

3.10.5 Number of Non-Missing Elements

3.11 Create New Variables

3.12 Create a Data Frame

3.13 Recode Variables

3.14 Rename Variables

3.15 Convert the Types of Variables

3.16 Calculations

3.16.1 Historical Actual Player Statistics

3.16.1.1 Career Statistics

3.16.1.2 Season-by-Season Statistics

3.16.1.3 Week-by-Week Statistics

3.16.2 Historical Actual Fantasy Points

3.16.3 Player Age

3.17 Plotting

3.17.1 Rushing Yards per Carry By Player Age

3.17.2 Defensive and Offensive EPA per Play

Feedback

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3.7.1 Is Equal To: `==`

3.7.2 Is Not Equal To: `!=`

3.7.3 Is Greater Than: `>`

3.7.4 Is Less Than: `<`

3.7.5 Is Greater Than or Equal To: `>=`

3.7.6 Is Less Than or Equal To: `<=`

3.7.7 Is In a Value of Another Vector: `%in%`

3.7.8 Is Not In a Value of Another Vector: `!(%in%)`

3.7.9 Is Missing: `is.na()`

3.7.10 Is Not Missing: `!is.na()`

3.7.11 And: `&`

3.7.12 Or: `|`

3.8.3.1 Base `R`

3.8.4.1 Base `R`

3.8.5.1 Base `R`